This paper explores a bistatic Integrated Sensing and Communication (ISAC) framework, where a base station transmits communication signal that serve both direct communication with a user and multi-target parameter estimation through reflections captured by a separate sensing receiver. We assume that the instantaneous knowledge of the transmit signal at the sensing receiver is not available, and the sensing receiver only has knowledge of the statistical properties of the received signal. Unlike prior research that focuses on power allocation or optimal beamforming design for ISAC, we emphasize the inadequacy of the Cramér-Rao Bound (and its variant) in low Signal-to-Noise Ratio (SNR) regimes, particularly in passive sensing scenarios. Due to severe path loss and other impairments, the received sensing SNR is often significantly lower than that of direct Line-of-Sight communication, making CRB-based performance evaluation unreliable. To address this, we adopt the Ziv-Zakai Bound (ZZB) for Angle of Arrival estimation, which provides a more meaningful lower bound on estimation error. We derive analytical expressions for the ZZB and the achievable ergodic communication rate as functions of SNR. Through numerical simulations, we analyze the pareto-front between communication and sensing performance, demonstrating why ZZB serves as a better metric in low sensing SNR ISAC where traditional CRB-based approaches fail.

翻译：本文探讨了一种双基地集成感知与通信框架，其中基站发射的通信信号既用于与用户的直接通信，又通过独立感知接收器捕获的反射信号实现多目标参数估计。我们假设感知接收器无法获取发射信号的瞬时信息，仅掌握接收信号的统计特性。与以往聚焦于ISAC功率分配或最优波束成形设计的研究不同，本文重点揭示了克拉美-罗界（及其变体）在低信噪比环境下（特别是在无源感知场景中）的局限性。由于严重的路径损耗及其他损伤，接收到的感知信噪比通常显著低于直接视距通信的信噪比，这使得基于CRB的性能评估不可靠。为此，我们采用Ziv-Zakai界进行到达角估计，该界限为估计误差提供了更具实际意义的理论下界。我们推导了ZZB和可达遍历通信速率随信噪比变化的解析表达式。通过数值仿真，我们分析了通信与感知性能之间的帕累托前沿，论证了在传统CRB方法失效的低感知信噪比ISAC场景中，ZZB为何能成为更有效的评估指标。